notebooks/attachments.ipynb

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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<td>\n",
        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
        "</td>"
      ],
      "metadata": {
        "id": "z-Oj3xf99mKw"
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    {
      "cell_type": "markdown",
      "source": [
        "<td>\n",
        "<a href=\"https://colab.research.google.com/drive/1JuT867kb7ZwbaYYJYSRVQYf58-0GjSzf\" target=\"_blank\"><img\n",
        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
        "</td>\n",
        "\n",
        "<td>\n",
        "<a href=\"https://github.com/Labelbox/labelpandas/blob/main/notebooks/attachments.ipynb\" target=\"_blank\"><img\n",
        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
        "</td>"
      ],
      "metadata": {
        "id": "HDAJUJeo9oi2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# _**Creating Data Rows with Attachments with LabelPandas**_"
      ],
      "metadata": {
        "id": "5AYNC8v29z1Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## _**Documentation**_"
      ],
      "metadata": {
        "id": "5cbHfXSZ-mOI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Rows**\n",
        "_____________________"
      ],
      "metadata": {
        "id": "LliqTKrMszpJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Requirements:**\n",
        "\n",
        "- A `row_data` column - This column must be URLs that point to the asset to-be-uploaded\n",
        "\n",
        "- Either a `dataset_id` column or an input argument for `dataset_id`\n",
        "  - If uploading to multiple datasets, provide a `dataset_id` column \n",
        "  - If uploading to one dataset, provide a `dataset_id` input argument\n",
        "    - _This can still be a column if it's already in your CSV file_\n",
        "\n",
        "**Recommended:**\n",
        "- A `global_key` column\n",
        "  - This column contains unique identifiers for your data rows\n",
        "  - If none is provided, will default to your `row_data` column\n",
        "- An `external_id` column\n",
        "  - This column contains non-unique identifiers for your data rows\n",
        "  - If none is provided, will default to your `global_key` column  \n",
        "\n",
        "**Optional:**\n",
        "- A `project_id` columm or an input argument for `project_id`\n",
        "  - If batching to multiple projects, provide a `project_id` column\n",
        "  - If batching to one project, provide a `project_id` input argument\n",
        "    - _This can still be a column if it's already in your CSV file_"
      ],
      "metadata": {
        "id": "cwEpHUby952F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Attachments**\n",
        "_____________________"
      ],
      "metadata": {
        "id": "TuVbore5syCU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For attachments, the column name must be \" `attachment` + `divider` + `attachment_type` + `divider` + `column_name` \"\n",
        "  - Example: `attachment///raw_text///sample_column_name`\n",
        "  - `attachment_type` must be one of the following:\n",
        "    - `image`, `video`, `raw_text`, `html`, `text_url`\n",
        "\n",
        "\n",
        "Values for attachments must correspond with the attachment type per Labelbox docs\n",
        "  - More here: \n",
        "    - [Labelbox docs on attachments](https://docs.labelbox.com/docs/asset-attachments)"
      ],
      "metadata": {
        "id": "YZZjlfVbsvj-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## _**Code**_"
      ],
      "metadata": {
        "id": "c_fVJPX393rp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install LabelPandas"
      ],
      "metadata": {
        "id": "x5VXUdFE--XI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LOZffjDK3cHG"
      },
      "outputs": [],
      "source": [
        "## Install LabelPandas\n",
        "!pip install labelpandas --upgrade -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import labelpandas as lp\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "sRozyJUX9YVy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define runtime variables"
      ],
      "metadata": {
        "id": "Zuckb5Bf_LLw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "csv_path = \"https://raw.githubusercontent.com/Labelbox/labelpandas/main/datasets/attachments.csv\" # Path to your CSV file\n",
        "api_key = \"\" # Labelbox API Key"
      ],
      "metadata": {
        "id": "QwZysJDY_Mvf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load a CSV"
      ],
      "metadata": {
        "id": "_qMWUpby_Bb6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(csv_path)\n",
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "fQYQZ-eR9fWW",
        "outputId": "d94e68c7-4989-4d54-865b-5b1a7926b23a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-de864794-d1e8-4f63-a087-bbe53517a4f7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-de864794-d1e8-4f63-a087-bbe53517a4f7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a Dataset (for demonstration purposes only)"
      ],
      "metadata": {
        "id": "4hM3xvVm_x7w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client = lp.Client(lb_api_key=api_key)"
      ],
      "metadata": {
        "id": "wKenzu7R_0iD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datset_id = client.lb_client.create_dataset(name=\"LabelPandas-attachments\").uid"
      ],
      "metadata": {
        "id": "6xY4IfcS_2Vh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upload to Labelbox"
      ],
      "metadata": {
        "id": "3WTZj-UJ4T-m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = client.create_data_rows_from_table(\n",
        "    table = df,\n",
        "    dataset_id = datset_id,\n",
        "    skip_duplicates = False, # If True, will skip data rows where a global key is already in use,\n",
        "    verbose = True, # If True, prints information about code execution\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imPBhZDh_KeH",
        "outputId": "3f791952-16cf-4813-d275-555717a864b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating upload list - 10 rows in Pandas DataFrame\n",
            "Beginning data row upload for dataset ID cle91kpv60p1n07uv3y1r2y3l: uploading 10 data rows\n",
            "Batch #1: 10 data rows\n",
            "Success: Upload batch number 1 successful\n",
            "Upload complete - all data rows uploaded\n"
          ]
        }
      ]
    }
  ]
}